Abstract

This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of learning kernel parameters, and (ii) a probabilistic interpretation (e.g., variance in prediction) that is informative for better understanding of the MIL prediction problem. The bag labeling protocol of the MIL problem, namely the existence of a positive instance in a bag, can be effectively represented by a sigmoid likelihood model through the max function over GP latent variables. To circumvent the intractability of exact GP inference and learning incurred by the noncontinuous max function, we suggest two approximations: first, the soft-max approximation; second, the use of witness indicator variables optimized with a deterministic annealing schedule. The effectiveness of GPMIL against other state-of-the-art MIL approaches is demonstrated on several benchmark MIL datasets.